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Last week, data science company RapidMiner launched a next-gen cloud platform for enterprises. RapidMiner builds a software platform for data science teams that unites data preparation, machine learning, predictive model deployment and more.
The company’s platform has been re-architected from the ground up to deploy models faster, bridge the gap between data science and business understanding, etc. It also helps scale the enterprise environment by connecting to common data sources, code-free deployment and more.
In terms of benefits, the company said the platform would reduce friction in enterprise deployments for customers who prefer a cloud-first environment. Also, it helps companies scale easily and seamlessly adapt to changes in their architecture, policies, and personnel.
Check out RapidMiner’s next-gen cloud platform here.
RapidMiner is founded by data scientists Ingo Mierswa and Ralf Klinkenberg. Initially, the duo developed a tool for students, professors, instructors, and researchers. Today, the tool is used by more than 4,000 universities worldwide.
Transforming enterprises
With this latest launch, RapidMiner will transform itself from an academic, AI/ML, data-focused platform to an enterprise-driven one.
But, the question is, why now? The company said it saw many challenges that prevented it from fulfilling the promise of AI in its organisations. “As a data science platform, we have always wanted to make AI more accessible so that anyone can positively impact the future,” it added.
The launch of its next-gen cloud platform comes after its recent rebranding.
The journey of RapidMiner
Recalling his early days, Mierswa said he had created a churn prediction model for the largest telecommunications company in Italy and worked tirelessly for six months on a model that was supposed to save the company tons of money. But, unfortunately, that model never made it into production, he added.
“Why? Because I failed to communicate what I was doing and how I was doing it,” said Mierswa.
“For me, it’s easy to trust mathematics, but for others, code is its own kind of black box,” he added.
After many failed projects later, Mierswa met Kinkenberg, another aspiring data scientist who wanted to demystify AI. This was the start of RapidMiner – a tool that supplements coding with higher-level, easier-to-understand modalities that brings together people at different skill levels.
Bridging the gap between data science and business
In 2013, the company was first named a leader in the Gartner Magic Quadrant, and in 2017, it was named in the Forrester report as a leader. “Today, we have nearly one million users—even if many of them started using RapidMiner for learning data science, they now rely on our platform to support all the analytics work that powers their business every day,” said Mierswa.
While becoming data-driven was a key initiative for most businesses, and many leaders began to understand the types of problems they could solve using predictive analytics, there is a lingering gap between data science and business knowledge, said Mierswa.
Besides these gaps, there is also a lack of trust in solutions that prevents even the promising models from being deployed. Given the complex architectures, IT infrastructure, and policies that most enterprises have, it is extremely challenging to embed models where they can make their most useful predictions, he said.
What next?
“Now that we have journeyed from being a student-friendly teaching platform to a platform that supports some of the world’s leading enterprises, we have gotten a much closer look at the challenges that prevent businesses from extracting real value from their data,” said Mierswa.
He said that no one knows what the future of data science looks like. “I envision a future where these different modalities no longer exist and are replaced by something even more powerful and user-friendly than what we have today. That’s what RapidMiner is working toward,” said Mierswa.
